class_docstr: Module that disentangle a fast-moving object from the background and performs deblurring.
  
  This is based on the original code from paper "DeFMO: Deblurring and Shape Recovery
      of Fast Moving Objects". See :cite:`DeFMO2021` for more details.
  
  Args:
      pretrained: Download and set pretrained weights to the model. Default: false.
  Returns:
      Temporal super-resolution without background.
  Shape:
      - Input: (B, 6, H, W)
      - Output: (B, S, 4, H, W)
  
  Examples:
      >>> import kornia
      >>> input = torch.rand(2, 6, 240, 320)
      >>> defmo = kornia.feature.DeFMO()
      >>> tsr_nobgr = defmo(input) # 2x24x4x240x320
code: def __init__(self, pretrained: bool = False) -> None:
      super().__init__()
      self.encoder = EncoderDeFMO()
      self.rendering = RenderingDeFMO()
  
      # use torch.hub to load pretrained model
      if pretrained:
          pretrained_dict = torch.hub.load_state_dict_from_url(
              urls["defmo_encoder"], map_location=map_location_to_cpu
          )
          self.encoder.load_state_dict(pretrained_dict, strict=True)
          pretrained_dict_ren = torch.hub.load_state_dict_from_url(
              urls["defmo_rendering"], map_location=map_location_to_cpu
          )
          self.rendering.load_state_dict(
              pretrained_dict_ren, strict=True
          )
      self.eval()
  
imports: from typing import Callable, Dict, List, Optional, Type
  import torch
  from torch import nn
  from kornia.core import Module, Tensor, concatenate, stack
  from kornia.utils.helpers import map_location_to_cpu
label_desc: Use image node_id 1 to create a new DeFMO object from inherited base classes: Module with example: obj = DeFMO(pretrained)